Recent advances of molecular dynamics simulations in nanotribology

Analysis of Tribology Properties of Trimethylolpropane-based Lubricant by Molecular Dynamics

Currently, it is crucial for the lubricant formulation industry to explore cost-effective and environmentally friendly methodologies for analyzing the tribological properties of engine aviation lubricants under high-temperature and high-pressure operating conditions. This study demonstrates the feasibility of employing molecular dynamic simulations to gain essential insights into the evolution of the tribological properties of lubricants during operation. A three-layer molecular model was devised, comprising nickel aluminide molecules in the top and bottom layers, and polyol ester in the core. The impact of sliding velocities ranging from 20 km/h to 100 km/h was investigated under varying temperature and pressure conditions. Concentration, temperature and velocity profiles, radial distribution function, mean square displacement, and friction coefficient were calculated and analyzed in detail. Notably, the highest friction coefficients - ranging from 2.5 to 0.75 - were observed at the lowest temperature and pressure conditions tested. Conversely, other sections of the gas turbine exhibited substantially lower friction coefficients - ranging from 0 to 0.01.Simulations demonstrate that increasing pressure and temperature reduce polymer chain mobility, leading to stronger internal interactions within the lubricant. Consequently, lubricant adsorption onto metal surfaces decreases. Furthermore, the lubricant performs exceptionally well when its molecules encounter higher velocities and temperatures. Based on the results obtained, the research demonstrates that the presented technique provides both quantitative and qualitative tribological information essential for understanding a system molecular behavior, serving as a guiding framework for researchers in the field.

Study of the Cold Curing Characteristics of Isocyanate-Modified Asphalt

Isocyanate esters are widely recognized for their superior curing capabilities. Leveraging this attribute, the current research formulated a modified cold-mixed asphalt blend using 4,4'-methylene diphenyl diisocyanate (MDI). Tests and analyses of the MDI-modified asphalt with varying inclusion percentages of MDI revealed that a mixture containing 15% rock asphalt and 15% MDI-modified asphalt exhibited a more balanced, comprehensive performance. We also conducted an examination of the role and properties of MDI in asphalt modification using molecular dynamics simulations. The cold-curing properties of MDI-modified asphalt as compared to petroleum asphalt were evaluated based on its density, free volume analysis, cohesive energy density, and glass transition temperature. Implementing the LB-13 gradation-a cold-mixed asphalt gradation with a nominal particle size of 13.2 mm recommended by Chinese specifications-we prepared MDI-modified cold-mixed asphalt and carried out analyses of its mechanical characteristics, high-temperature performance, and water damage resistance. The results demonstrated that MDI-modified asphalt showcases excellent ductility, flexibility, and aging resistance, surpassing the performance of petroleum asphalt. The stability, high-temperature rutting, and water damage resistance of the MDI-modified cold-mixed asphalt exceeded the requirements for hot-mixed asphalt. This research provides theoretical and experimental support for isocyanate ester applications in asphalt engineering, presenting significant value for practical engineering applications.

Coupling at the molecular scale between the graphene nanosheet and water and its effect on the thermal conductivity of the nanofluid

Graphene nanofluid is a promising way to improve heat transfer in many situations. As a two-dimensional material, graphene's anisotropic thermal conductivity influences the heat transfer of nanofluids. In the present study, a nonequilibrium molecular dynamics (MD) simulation is adopted to study the interaction between graphene nanosheets (GNSs) and liquid water in water-based graphene nanofluids. Consequently, the coupling interaction between the orientation and length of GNSs and the thermal conductivity of nanofluids is then investigated. We discover that the molecular thermal coupling between GNSs and water can effectively influence the orientation angle of the GNSs. A preferential orientation angle of the GNSs inside the nanofluid is then observed during heat transfer. The preferential orientation angle decreases with the GNS length and has no apparent relation with the size of heat flux in this study. The overall thermal conductivity of the nanofluid decreases as the orientation angle of the GNS rises. Increasing the GNS length not only reduces the preferential orientation angle but also improves the thermal conductivity along the graphene length direction. The thermal conductivity of the nanofluid along the graphene length direction increases from 0.414 to 4.085 W m K-1 as the length increases from 103 to 3274 A. Our results provide the fundamental knowledge of the heat transfer performance of graphene nanofluids.

Performance enhancement and optimization of residential air conditioning system in response to the novel FAl2O3-POE nanolubricant adoption

This paper aims to evaluate residential air conditioning systems' performance enhancement and optimization by adopting a novel functionalized Al2O3 (FAl2O3)-Polyolester (POE) nanolubricant. Comprehensive discussions were conducted on key performance parameters, including heat absorption, compressor work, cooling capacity, coefficient of performance (COP), and power consumption. Novel FAl2O3 nanoparticles were dispersed into the POE lubricant using a two-step method. The findings reveal that FAl2O3-POE nanolubricant exhibits superior heat absorption compared to pure POE. Heat absorption decreases with an increased initial refrigerant charge, while cooling capacity performance improves with an increased initial refrigerant charge. The COP shows an increasing trend at all concentrations of FAl2O3-POE nanolubricant when operating with R32. FAl2O3-POE/R32 demonstrates an enhanced range of 3.12%-32.26% for COP. The results suggest that applying novel FAl2O3-POE nanolubricant with R32 can reduce electrical power consumption by 13.79%-19.35%. The central composite design (CCD) offers an optimal condition for FAl2O3-POE nanolubricant with a concentration of 0.11 vol%, an initial refrigerant charge of 0.442 kg, resulting in a COP of 3.982, a standard error of 0.019, and a desirability of 1.0.

A Review of the Mechanical Properties of Graphene Aerogel Materials: Experimental Measurements and Computer Simulations

Graphene aerogels (GAs) combine the unique properties of two-dimensional graphene with the structural characteristics of microscale porous materials, exhibiting ultralight, ultra-strength, and ultra-tough properties. GAs are a type of promising carbon-based metamaterials suitable for harsh environments in aerospace, military, and energy-related fields. However, there are still some challenges in the application of graphene aerogel (GA) materials, which requires an in-depth understanding of the mechanical properties of GAs and the associated enhancement mechanisms. This review first presents experimental research works related to the mechanical properties of GAs in recent years and identifies the key parameters that dominate the mechanical properties of GAs in different situations. Then, simulation works on the mechanical properties of GAs are reviewed, the deformation mechanisms are discussed, and the advantages and limitations are summarized. Finally, an outlook on the potential directions and main challenges is provided for future studies in the mechanical properties of GA materials.

A review of recent advances and applications of machine learning in tribology

In tribology, a considerable number of computational and experimental approaches to understand the interfacial characteristics of material surfaces in motion and tribological behaviors of materials have been considered to date. Despite being useful in providing important insights on the tribological properties of a system, at different length scales, a vast amount of data generated from these state-of-the-art techniques remains underutilized due to lack of analysis methods or limitations of existing analysis techniques. In principle, this data can be used to address intractable tribological problems including structure-property relationships in tribological systems and efficient lubricant design in a cost and time effective manner with the aid of machine learning. Specifically, data-driven machine learning methods have shown potential in unraveling complicated processes through the development of structure-property/functionality relationships based on the collected data. For example, neural networks are incredibly effective in modeling non-linear correlations and identifying primary hidden patterns associated with these phenomena. Here we present several exemplary studies that have demonstrated the proficiency of machine learning in understanding these critical factors. A successful implementation of neural networks, supervised, and stochastic learning approaches in identifying structure-property relationships have shed light on how machine learning may be used in certain tribological applications. Moreover, ranging from the design of lubricants, composites, and experimental processes to studying fretting wear and frictional mechanism, machine learning has been embraced either independently or integrated with optimization algorithms by scientists to study tribology. Accordingly, this review aims at providing a perspective on the recent advances in the applications of machine learning in tribology. The review on referenced simulation approaches and subsequent applications of machine learning in experimental and computational tribology shall motivate researchers to introduce the revolutionary approach of machine learning in efficiently studying tribology.